Changes In Vegetation Greenness Research Articles

Mechanism of Vegetation Greenness Change and Its Correlation with Terrestrial Water Storage in the Tarim River Basin

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of <0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation.

The greening of vegetation on the Loess Plateau has resulted in a northward shift of the vegetation greenness line

Vegetation greenness is crucial for assessing vegetation dynamics and evaluating the effectiveness of ecological governance on the Loess Plateau. In this study, we utilized Google Earth Engine (GEE) platform and Landsat image to investigate the spatiotemporal evolution of vegetation greenness and the shift of vegetation greenness lines (VGLs) during the growing season on the Loess Plateau from 1987 to 2020. The driving factors were analyzed using a geographic detector. The results show that the average annual growth rates of NDVI and EVI in the Loess Plateau growing season from 1987 to 2020 were 0.0042/year (P < 0.01) and 0.0023/year (P < 0.01), respectively, with the growth rate after 2000 being three to four times higher. Spatially, significant increases of 78.8% in NDVI and 69.2% in EVI were observed across the entire area of the Loess Plateau. Furthermore, the average VGL based on NDVI and EVI on the Loess Plateau from 1987 to 2020 exhibited a northwestward shift at a rate of 9.79 km/year. This shift resulted in an average movement of 271.83 km over the period. The most significant shifts occurred between 2005 and 2010. Land use and precipitation emerged as the predominant driving factors influencing both the change in vegetation greenness and the shift of the VGLs. Land use and precipitation are the primary factors influencing changes in vegetation greenness and the shift of the VGLs. Land use explains 49.7% of the variation in vegetation greenness, while precipitation accounts for 43%. The combined influence of land use and precipitation explains 68% of the variation in vegetation greenness on the Loess Plateau. Prolonged growing seasons due to climate changes and the “Grain for Green” Project significantly contributed to increased vegetation greenness and northward VGL shifts. Our study offers valuable insights into the spatiotemporal dynamics of vegetation greenness and the shift of VGLs, enhancing our understanding and providing guidance for effective vegetation restoration efforts on the Loess Plateau.

Strong Green‐Up of Tropical Asia During the 2015/16 El Niño

AbstractEl Niño/Southern Oscillation (ENSO) is the main climate mode that drives the interannual variability in climate and consequently vegetation greenness. While widespread green‐up has been reported and examined in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used four remote sensing‐based leaf area index (LAI) products to investigate changes in vegetation greenness during the 2015/16 El Niño in tropical Asia. We found a strong green‐up during the 2015/16 El Niño in tropical Asia, with its regional average LAI stronger than that of tropical America. The drivers for the green‐up vary across the region, with radiation being the main driver for continental tropical Asia, and temperature and soil water anomalies in the west and east parts of maritime tropical Asia, respectively. These findings provide important insights into the response of tropical Asia's vegetation to extreme climate anomalies.

Greenness change associated with construction and operation of photovoltaic solar energy in China

Photovoltaic (PV) solar energy is considered as a promising solution to mitigate the environmental costs associated with the use of fossil fuels. However, the environmental impacts of constructing and operating PV solar energy remain unclear. This study assesses the environmental consequences of PV construction and operation by examining changes in vegetation greenness on a national scale in China, where PV solar energy has rapidly expanded. Utilizing 30-m vegetation indices and PV maps, we discover that the construction of PV facilities could significantly reduce greenness, with the magnitude of the impacts varying according to local greenness levels. Forests were found to suffer the greatest adverse impacts, while barrens were less affected. Small and medium-sized PV facilities, often established in regions with high levels of greenness, exhibited a stronger negative influence on greenness compared to larger ones. However, during the operational phase, an increase in vegetation greenness is generally observed in PV facilities, particularly in barrens. The magnitude of green recovery may vary considerably across different facilities, with approximately 21 % of PV facilities showing an improvement in greenness compared to their pre-construction levels. This study provides a reference for guiding the synergistic development of renewable energy and environmental protection.

Changes in albedo and its radiative forcing of grasslands in East Asia drylands

BackgroundGrasslands in drylands are increasingly influenced by human activities and climate change, leading to alterations in albedo and radiative energy balance among others. Surface biophysical properties and their interactions change greatly following disturbances. However, our understanding of these processes and their climatic impacts remains limited. In this study, we used multi-year observations from satellites and eddy-covariance towers to investigate the response of albedo to variables closely associated with human disturbances, including vegetation greenness (EVI) and surface soil volumetric water content (VWC), as well as snow cover and clearness index (Ta) for their potential relationships.ResultsEVI and VWC during the growing season were the primary factors influencing albedo. EVI and VWC were negatively correlated with albedo, with VWC’s total direct and indirect impacts being slightly smaller than those of EVI. During the non-growing season, snow cover was the most influential factor on albedo. VWC and Ta negatively affected albedo throughout the year. We estimated the impact of variations in EVI and VWC on climate to be in the range of 0.004 to 0.113 kg CO2 m−2 yr−1 in CO2 equivalent.ConclusionsThis study indicates the significant impacts of climate change and human disturbances on vulnerable grassland ecosystems from the perspective of altered albedo. Changes in vegetation greenness and soil properties induced by climate change and human activities may have a substantial impact on albedo, which in turn feedback on climate change, indicating that future climate policies should take this factor into consideration.

Spatial Heterogeneity of Combined Factors Affecting Vegetation Greenness Change in the Yangtze River Economic Belt from 2000 to 2020

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect.

Multiple Greenness Indexes Revealed the Vegetation Greening during the Growing Season and Winter on the Tibetan Plateau despite Regional Variations

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales.

Mapping crop phenophases in reproductive growth period by satellite solar-induced chlorophyll fluorescence: A case study in mid-temperate zone in China

Extracting crop phenophases from satellite remote sensing data is crucial for managing agricultural activities and estimating crop yield over large scales. The traditional Vegetation Indices (VIs), such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), primarily indicate changes in vegetation greenness, which characterize crop phenophases well in vegetative growth period but may be difficult to associate with phenophases in reproductive growth period that are more related with physiology status. In this study, we investigated the potential of the satellite Solar-Induced chlorophyll Fluorescence (SIF) on extracting the crop phenophases in reproductive growth period, i.e., milk-ripe phase and maturity phase, using single-season cropland in mid-temperate zone in China as a test bed. We found that SIF outperformed EVI and NDVI in extracting milk-ripe phase and maturity phase of maize, rice and wheat using double logistic method. In particular, SIF-derived maturity phase were closer to field-observed phenophases (Mean Bias = 0.73 days) with higher R2 (0.87) than that from EVI and NDVI. At a regional scale, the milk-ripe phase and maturity phase were observed from August to mid-September, and from mid-September to mid-October, respectively, which varied with crop types and in spatial distribution. Out of all crops in mid-temperate zone in China, 65% experienced a delay in the milk-ripe phase, whereas 77% exhibited a delay in the maturity phase from 2001 to 2019. In addition to the adjustment of human managements under climate warming, we further found that crop phenophases in reproductive growth period exhibited the strongest correlation with preseason water-related environmental variables, particularly vapor pressure deficit and total precipitation. This work highlighted the potential of satellite SIF in identifying the crop-specific milk-ripe and maturity phases, and improving our understanding for spatio-temporal variations of crop phenophases in reproductive growth period as well as their responses to preseason environmental variables, which will in turn promote SIF applications in agriculture.

Changes in vegetation greenness and its response to precipitation seasonality in Central Asia from 1982 to 2022

Central Asia is the world’s largest azonal arid region, with strong seasonal precipitation patterns. Vegetation in this region is relatively sparse and extremely sensitive to climatic changes. However, long-term trends in vegetation in Central Asia are still unclear or even controversially recognized, hindering the assessment of climate change’s impact on regional sustainability. Here, we present the longest time series of vegetation index in Central Asia and investigated its response to precipitation seasonality from 1982 to 2022 by integrating normalized difference vegetation index data from the Global Inventory Monitoring and Modeling Studies and the Moderate Resolution Imaging Spectroradiometer. The results indicate a greening trend during 1982–2000 and a browning trend during 2000–2008. In contrast to previous studies, we detected a rapid greening trend during 2008–2022, largely resulted from a continuous warm-wet trend in Central Asia. In addition, strong spatial variation in vegetation is uncovered within the region, suggesting spatial differences in vegetation responding to contrasting precipitation seasonality. Under CMIP6 climate scenarios, spring wetting and summer drying are projected to prompt Central Asian vegetation change to a simultaneous greening south and browning north.

Human Activities Accelerated Increase in Vegetation in Northwest China over the Three Decades

Natural ecosystems are changing more quickly because of human activities, the type and intensity of which are directly correlated with vegetation greenness. To effectively determine how human activities affect trends in vegetation under climate change, we must differentiate between various types of human activities. The GTWR model can study the spatiotemporal non-stationary relationship between the NDVI trend and climate change. The GTWR model was incorporated into multiple climate variables and improved residual analysis to quantify the contributions of climate change and human activities on vegetation change trends in the Hexi region during different periods. This study divides human activities into four groups based on land use change: urbanization, agricultural expansion, desertification, and ecological restoration to further investigate their contribution to vegetation greenness change. The results showed that in 56.9% of the significant vegetation greening trends between 1982 and 2015, climate factors contributed only 7.4%, while human factors contributed a significant 22.7%. Since the ecological restoration project implemented in 2000, the expansion intensity of ecological restoration and urbanization increased significantly, followed by agricultural expansion and desertification. For the considerable greening trends in the Hexi region, the ecological restoration project contributed 26.7%, while agricultural expansion and urbanization contributed 17.5% and 4.6%, respectively. This study aims to provide new insights for more accurate simulation and evaluation of the interaction effects of climate change and human socio-economic development on vegetation growth.

Vegetation and Built-Up Area Monitoring in Bandung City Using Multitemporal Imagery

Bandung is West Java's largest metropolitan city and Indonesia's third largest. The city of Bandung is very strategic in various aspects, such as accessibility, communication, public facilities, and the economy. The Increased population in Bandung indicates more complex ongoing human activities, which can then affect changes in land use. The land covers in urban areas tends to change more drastically over a short period e than in rural areas because of rapid urbanization. Therefore, urban phenomenon changes are ideally monitored and detected from satellite images with a multitemporal resolution. Vegetation greenness and built-up areas can identify through multitemporal remote sensing imagery. Changes in vegetation and built-up area can monitor using remote sensing with multitemporal imagery. The analysis of changes in vegetation and built-up area studied in Bandung City represents an area with rapid population growth. This study aims to: 1) Identify changes in vegetation greenness in Bandung City between 2014 and 2021, 2) Identify built-up area changes in Bandung City between 2014 and 2021, 3) Analyze the relevance between vegetation greenness and the built-up area in Bandung City—the correlation between NDBI and NDVI through selected samples is representative of all data in Landsat 8 imagery. The proportion between the values of NDBI and NDVI samples is 0.9034. So, it is concluded that the two variables are positively correlated. Therefore, the study’s results recommend preserving vegetated land cover to conserve natural resources and prevent increased land surface temperature.Keywords: Remote Sensing Imagery, Built-Up Area, Vegetation Greenness, Bandung City

Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region

ABSTRACT The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.

Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021.

Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×104 km2, and browning was 5.32×104 km2, representing a quarter and 2.86% of the natural vegetation area, respectively. The results of the browning driver analysis show that areas with high altitude, reduced annual precipitation, high intensity of human activity, average annual maximum and average annual minimum precipitation of approximately 500mm are most susceptible to browning on the QTP. For the seven different vegetation types, their top 6 most important browning drivers and the ranking of drivers differed. DEM and precipitation changes are important drivers of browning for seven vegetation types. These results reflect the latest spatial and temporal dynamics of vegetation on the QTP and highlight the common and characteristic browning drivers of vegetation ecosystems. They provide support for understanding the response of different vegetation to natural and human impacts and for further implementation of site-specific restoration measures.

Wind turbine wakes can impact down-wind vegetation greenness

Global wind energy has expanded 5-fold since 2010 and is predicted to expand another 8–10-fold over the next 30 years. Wakes generated by wind turbines can alter downwind microclimates and potentially downwind vegetation. However, the design of past studies has made it difficult to isolate the impact of wake effects on vegetation from land cover change. We used hourly wind data to model wake and non-wake zones around 17 wind facilities across the U.S. and compared remotely-sensed vegetation greenness in wake and non-wake zones before and after construction. We located sampling sites only in the dominant vegetation type and in areas that were not disturbed before or after construction. We found evidence for wake effects on vegetation greenness at 10 of 17 facilities for portions of, or the entire growing season. Evidence included statistical significance in Before After Control Impact statistical models, differences >3% between expected and observed values of vegetation greenness, and consistent spatial patterns of anomalies in vegetation greenness relative to turbine locations and wind direction. Wakes induced both increases and decreases in vegetation greenness, which may be difficult to predict prior to construction. The magnitude of wake effects depended primarily on precipitation and to a lesser degree aridity. Wake effects did not show trends over time following construction, suggesting the changes impact vegetation greenness within a growing season, but do not accrue over years. Even small changes in vegetation greenness, similar to those found in this study, have been seen to affect higher trophic levels. Given the rapid global growth of wind energy, and the importance of vegetation condition for agriculture, grazing, wildlife, and carbon storage, understanding how wakes from wind turbines impact vegetation is essential to exploit or ameliorate these effects.

Changes in Vegetation Greenness and Their Influencing Factors in Southern China

Since the 21st century, China has experienced rapid development, and the spatial and temporal changes in vegetation cover have become increasingly significant. Southern China is a representative region for human activities, climate change, and vegetation change, but the current human understanding of the interactions between vegetation and its influencing factors is still very limited. In our study, we use NDVI as the vegetation greenness data, land cover data, temperature, precipitation, downgradient shortwave radiation, and CO2 data to investigate the interrelationship among vegetation, climate change, and human activities in southern China. The changes and their consistency were studied by trend analysis and Hurst exponent analysis. Then, the contribution of each influencing factor from 2001 to 2020 was quantified by random forest. The results showed that the vegetation in southern China showed an overall rising trend, and areas with a continuous changing trend were concentrated in the Pearl River Delta, western Guangdong, and eastern Guangdong, with a growth rate of 0.02∼0.04%. The vegetation in northern Guangdong did not change significantly. The main factor of NDVI spatial variation in southern China is the land-use factor, accounting for 79.4% of the variation, while climate factors produce further differences. The contributions and lagged effects of NDVI factors on different land-use types and the lagged effects of different climate factors are different and are related to the climate and vegetation background in Sourthern China. Our study is useful in estimating the contribution of NDVI change by each considered factor and formulating environmentally friendly regional development strategies and promoting human–land harmony.

Climatic Niche of Vegetation Greenness Is Likely to Be Conservative in Degraded Land

Satellite data have been widely used to study changes in vegetation greenness in geographical space; however, this change is rarely considered in climatic space. Here, the climatic niche dynamics of vegetation greenness, represented by the normalized difference vegetation index (NDVI), was quantified in the climate space of the Loess Plateau, a piece of degraded land greening significantly from 2000 to 2018. The niche similarity test was used to examine the niche conservatism of vegetation greenness during the 19 years of restoration. The results show that the climate niche of vegetation greenness is always more similar than expected. The stability niche occupied most parts (83–98%) of their climatic niche, and niche overlap reached 0.52–0.69. Climate niche conservatism suggests that potential greenness constructed by statistical methods could be used as a criterion or baseline for ecosystem function restoration on the Loess Plateau. The study also suggests that the integrated niche similarity test in decision-making for restoration of degraded land will clarify our understanding of the climatic niche dynamics of vegetation greenness and the making of forecasts.

Land Management Explains the Contrasting Greening Pattern Across China‐Russia Border Based on Paired Land Use Experiment Approach

AbstractThe greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data‐driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border‐intensified agricultural development on China side (CNSP) versus largely little‐disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p &lt; 0.05), with the annual variability reaching values of 9.8 × 10−3 yr−1 and 11.3 × 10−3 yr−1, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8‐day periods from 26 June to 28 July. During this period, the LAI trend of CNSP is much higher than that of RUSP, at 92.7 × 10−3 yr−1 (p &lt; 0.01) and 43.8 × 10−3 yr−1 (p &lt; 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.

Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

The spatiotemporal response of China's vegetation greenness to human socio-economic activities

Climate change and human socioeconomic activities both strongly impact long-term vegetation greenness. It is more a challenge to evaluate the impacts of socioeconomic activities on vegetative greenness than climate change, partially due to the lack of appropriate quantitative indicators of the former. Here we examined the relationship between the remote sensing nighttime light (NTL) data and the Normalized Difference Vegetation Index (NDVI), which in this study are used as the proxies of socioeconomic activities and vegetation greenness, respectively. We first eliminated the vegetation greenness changes in response to climate change and calculated the human-activities-induced NDVI (HNDVI). After explored the spatiotemporal patterns of the HNDVI and NTL data across China from 1998 to 2018, we studied the relationship between the HNDVI and NTL at the grid and county levels, respectively. Our results show that the mean adjusted DN values of the NTL data (NTLI) continuously increase (+0.2938) across our study area from 1998 to 2018, whereas the HNDVI values fluctuate with a general upward trend (+0.0018). Most grids (91.2%) with increased HNDVI were found in rural areas, particularly in the Northeast forest shelterbelt and the Loess Plateau. By contrast, the HNDVI values in rapidly urbanized areas in Chinese major urban agglomerations mainly show a downward trend, especially in the Yangtze River Delta (YRD) urban agglomeration. The relationships between the NTLI and HNDVI are inconsistent over time and across space, which could be attributed to land use conditions, afforestation projects in rural areas, and greening activities in urban areas over different periods and regions.

Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions.